Evidence map›Paper›PMID 40355546›Full record

ArticleScientific reports2025

Validation of a software application using electronic health records for automatic detection of community onset sepsis.

Cristian Duré, Sandra Jonmarker, Eva Joelsson-Alm, Hampus Nordqvist, Katarina Bohm, Liivi Rimling, Mikael Andersson Franko, Maria Cronhjort, Kristian Ängeby

Abstract readValidation Study
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Cristian DuréDepartment of Clinical Science and Education, Karolinska Institutet, Stockholm, Sweden. cristian.dure@ki.se.
Sandra JonmarkerDepartment of Clinical Science and Education, Karolinska Institutet, Stockholm, Sweden.
Eva Joelsson-AlmDepartment of Clinical Science and Education, Karolinska Institutet, Stockholm, Sweden.
Hampus NordqvistDepartment of Clinical Science and Education, Karolinska Institutet, Stockholm, Sweden.
Katarina BohmDepartment of Clinical Science and Education, Karolinska Institutet, Stockholm, Sweden.
Liivi RimlingDepartment of Emergency Medicine, Södersjukhuset, Stockholm, Sweden.
Mikael Andersson FrankoDepartment of Clinical Science and Education, Karolinska Institutet, Stockholm, Sweden.
Maria Cronhjort *Department of Clinical Science and Education, Karolinska Institutet, Stockholm, Sweden.
Kristian Ängeby *Department of Clinical Science and Education, Karolinska Institutet, Stockholm, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Our aim was to design and validate a software application, based on the Sepsis-3 criteria, capable of retrospectively identifying community-onset sepsis among emergency department patients requiring hospital admission.The application was developed using QlikView (Qlik, King of Prussia, PA, USA) software, and accessed data from the electronic health records TakeCare (CompuGroup Medical, Koblenz, Germany), and CliniSoft (CliniSoft, Kuopio, Finland). The application utilized indicators such as blood culture data, antibiotic administration, and Sequential Organ Failure Assessment scores to detect sepsis cases according to Sepsis-3 criteria. The application was tested retrospectively against a cohort from a large city hospital in Stockholm over a 2-year period, and its performance was compared to physician record reviews in a subset of cases identified by stratified random sampling. The results showed that among 229,195 emergency department visits leading to 60,213 hospital admissions, the application detected 7027 cases of sepsis. Validation using physician record review of a random selection of 426 cases demonstrated a sensitivity, specificity, positive predictive value, and negative predictive value of 95%, 99%, 92%, and 99%, respectively. The lower respiratory tract was the most common site of infection. This software application effectively identified community-onset sepsis patients using electronic health record data with high performance. It has the potential to improve sepsis identification as it operates independently of diagnostic codes and may, therefore, facilitate research in many areas of sepsis. Furthermore, it can be used as a tool within the healthcare system to enhance sepsis surveillance and evaluate quality improvement interventions.

Indexed as

Community-Acquired InfectionsElectronic Health RecordsSepsisSoftwareAdultAgedAged, 80 and overEmergency Service, HospitalFemaleHumansMaleMiddle AgedRetrospective StudiesEDs Emergency DepartmentsEHR Electronic Health RecordICUs Intensive care unitsSepsisSOFA Sequential Organ Failure Assessment

Identifiers

PMID40355546
PMCPMC12069618

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.